Machine Learning & AI

Basic

Logistic Regression

Applies the sigmoid to a linear model to output a class probability.

Formula

\hat{y} = \dfrac{1}{1+e^{-(\mathbf{w}^\top \mathbf{x}+b)}}

Variables

\mathbf{w}Weights

\mathbf{x}Features

bBias

Example

linear score 0: probability = 0.5

Did You Know?

Despite the name, logistic regression is a classification algorithm, not a regression one.

classification model supervised

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More in Machine Learning & AI

Linear Regression Model

Predicts a continuous value as a weighted sum of input features plus a bias.

Gradient Descent Update

Iteratively moves parameters in the direction that most reduces the loss.

ReLU Activation

Rectified Linear Unit: outputs the input if positive, else zero.

Leaky ReLU

A ReLU variant that lets a small gradient flow for negative inputs.

Tanh Activation

Squashes input to the range (-1, 1); zero-centred activation.

Softmax

Turns a vector of scores into a probability distribution that sums to 1.